Creating a Cancer Screening Giant

A few days after shocking the AI and imaging center industries with its acquisitions of Aidence and Quantib, RadNet’s Friday investor briefing revealed a far more ambitious AI-enabled cancer screening strategy than many might have imagined.

Expanding to Colon Cancer – RadNet will complete its AI screening platform by developing a homegrown colon cancer detection system, estimating that its four AI-based cancer detection solutions (breast, prostate, lung, colon) could screen for 70% of cancers that are imaging-detectable at early stages.

Population Detection – Once its AI platform is complete, RadNet plans to launch a strategy to expand cancer screening’s role in population health, while making prostate, lung, and colon cancer screening as mainstream as breast cancer screening.

Becoming an AI Vendor – RadNet revealed plans to launch an externally-focused AI business that will lead with its multi-cancer AI screening platform, but will also create opportunities for RadNet’s eRAD PACS/RIS software. There are plenty of players in the AI-based cancer detection arena, but RadNet’s unique multi-cancer platform, significant funding, and training data advantage would make it a formidable competitor.

Geographic Expansion – RadNet will leverage Aidence and Quantib’s European presence to expand its software business internationally, as well as into parts of the US where RadNet doesn’t own imaging centers (RadNet has centers in just 7 states).

Imaging Center Upsides – RadNet’s cancer screening AI strategy will of course benefit its core imaging center business. In addition to improving operational efficiency and driving more cancer screening volumes, RadNet believes that the unique benefits of its AI platform will drive more hospital system joint ventures.

AI Financials – The briefing also provided rare insights into AI vendor finances, revealing that DeepHealth has been running at a $4M-$5M annual loss and adding Aidence / Quantib might expand that loss to $10M- $12M (seems OK given RadNet’s $215M EBITDA). RadNet hopes its AI division will become cash flow neutral within the next few years as revenue from outside companies ramp up.

The Takeaway

RadNet has very big ambitions to become a global cancer screening leader and significantly expand cancer screening’s role in society. Changing society doesn’t come fast or easy, but a goal like that reveals how much emphasis RadNet is going to place on developing and distributing its AI cancer screening platform going forward.

RadNet’s Big AI Play

Imaging center giant RadNet shocked the AI world this week, acquiring Dutch startups Aidence and Quantib to support its AI-enabled cancer screening strategy.

Acquisition Details – RadNet acquired Aidence for $40M-$50M and Quantib for $45M, positioning them alongside DeepHealth within its new AI division. Aidence’s Veye Lung Nodules solution (CT lung nodule detection) is used across seven European countries and has been submitted for FDA 510(k) clearance, while Quantib’s prostate and brain MRI solutions have CE and FDA clearance and are used in 20 countries worldwide.

RadNet’s Cancer Screening Strategy – RadNet sees a huge future for cancer screening and believes Aidence (lung cancer) and Quantib (prostate cancer) will combine with DeepHealth (breast cancer) to make it a population health screening leader. 

RadNet’s AI Screening History – Even if these acquisitions weren’t expected, they aren’t out of character for RadNet, which created its mammography AI portfolio through a series of 2019-2020 acquisitions (DeepHealth, Nulogix) and equity investments (WhiteRabbit.ai). Plus, acquisitions have been a core part of RadNet’s imaging center strategy since before we were even talking about AI.

Unanswered Questions – It’s still unclear whether RadNet will take advantage of Aidence / Quantib’s European presence to expand internationally or if RadNet will start selling its AI portfolio to other hospitals and imaging center chains.

Another Consolidation Milestone – All those forecasts of imaging AI market consolidation seem to be quickly coming true in 2022, following MaxQ’s pivot out of imaging and RadNet’s Aidence / Quantib acquisitions. It’s also becoming clearer what type of returns AI startups and VCs are willing to accept, as Aidance and Quantib sold for about 3.5-times and 5.5-times their respective venture funding ($14M & $8M) and Nanox acquired Zebra-Med for 1.7 to 3.5-times its VC funding ($57.4M).

The Takeaway

It seems that RadNet will leverage its newly-expanded AI portfolio to become the US’ premier cancer screening company. That would be a huge accomplishment if cancer screening volumes grow as RadNet is forecasting. However, RadNet’s combination of imaging AI expertise, technology, funding, and training data could allow it to have an even bigger impact beyond the walls of its imaging centers.

IBM Sells Watson Health

IBM is selling most of its Watson Health division to private equity firm Francisco Partners, creating a new standalone healthcare entity and giving both companies (IBM and the former Watson Health) a much-needed fresh start. 

The Details – Francisco Partners will acquire Watson Health’s data and analytics assets (including imaging) in a deal that’s rumored to be worth around $1B and scheduled to close in Q2 2022. IBM is keeping its core Watson AI tech and will continue to support its non-Watson healthcare clients.

Francisco’s Plans – Francisco Partners seems optimistic about its new healthcare company, revealing plans to maintain the current Watson Health leadership team and help the company “realize its full potential.” That’s not always what happens with PE acquisitions, but Francisco Partners has a history of growing healthcare companies (e.g. Availity, Capsule, GoodRx, Landmark Health) and there are a lot of upsides to Watson Health (good products, smart people, strong client list, a bargain M&A multiple, seems ideal for splitting up).

A Necessary Split – Like most Watson Health stories published over the last few years, news coverage of this acquisition overwhelmingly focused on Watson Health’s historical challenges. However, that approach seems lazy (or at least unoriginal) and misses the point that this split should be good news for both parties. IBM now has another $1B that it can use towards its prioritized hybrid cloud and AI platform strategy, and the new Watson Health company can return to growth mode after several years of declining corporate support.

Imaging Impact – IBM and Francisco Partners’ announcements didn’t place much focus on Watson Health’s imaging business, but it seems like the imaging group will also benefit from Francisco Partners’ increased support and by distancing itself from a brand that’s lost its shine. Even losing the core Watson AI tech should be ok, given that the Merge PACS team has increasingly shifted to a partner-focused AI strategy. That said, this acquisition’s true imaging impact will be determined by where the imaging group lands if/when Francisco Partners decides to eventually split up and sell Watson Health’s various units.

The Takeaway – The IBM Watson Health story is a solid reminder that expanding into healthcare is exceptionally hard, and it’s even harder when you wrap exaggerated marketing around early-stage technology and high-multiple acquisitions. Still, there’s plenty of value within the former Watson Health business, which now has an opportunity to show that value.

Duke’s Interpretable AI Milestone

A team of Duke University radiologists and computer engineers unveiled a new mammography AI platform that could be an important step towards developing truly interpretable AI.

Explainable History – Healthcare leaders have been calling for explainable imaging AI for some time, but explainability efforts have been mainly limited to saliency / heat maps that show what part of an image influenced a model’s prediction (not how or why).

Duke’s Interpretable Model – Duke’s new AI platform analyzes mammography exams for potentially cancerous lesions to help physicians determine if a patient should receive a biopsy, while supporting its predictions with image and case-based explanations. 

Training Interpretability – The Duke team trained their AI platform to locate and evaluate lesions following a process that human radiology educators and students would utilize:

  • First, they trained the AI model to detect suspicious lesions and to ignore healthy tissues
  • Then they had radiologists label the edges of the lesions
  • Then they trained the model to compare those lesion edges with lesion edges from an archive of images with confirmed outcomes

Interpretable Predictions – This training process allowed the AI model to identify suspicious lesions, highlight the classification-relevant parts of the image, and explain its findings by referencing confirmed images. 

Interpretable Results – Like many AI models, this early version could not identify cancerous lesions as accurately as human radiologists. However, it matched the performance of existing “black box” AI systems and the team was able to see why their AI model made its mistakes.

The Takeaway

It seems like concerns over AI performance are growing at about the same pace as actual AI adoption, making explainability / interpretability increasingly important. Duke’s interpretable AI platform might be in its early stages, but its use of previous cases to explain findings seems like a promising (and straightforward) way to achieve that goal, while improving diagnosis in the process.

AI Disparity Detection

Most studies involving imaging AI and patient race/ethnicity warn that AI might exacerbate healthcare inequalities, but a new JACR study outlines one way that imaging AI could actually improve care for typically underserved patients.

The AI vs. EHR Disparity Problem – The researchers used a deep learning model to detect atherosclerotic disease in CXRs from two cohorts of COVID-positive patients: 814 patients from a suburban ambulatory center (largely White, higher-income) and 485 patients admitted at an inner-city hospital (largely minority, lower-income). 

When they validated the AI predictions versus the patients’ EHR codes they found that:

  • The AI predictions were far more likely to match the suburban patients’ EHR codes than the inner-city patients’ EHR codes (0.85 vs. 0.69 AUCs)
  • AI/EHR discrepancies were far more common among patients who were Black or Hispanic, prefer a non-English language, and live in disadvantaged zip codes

The Imaging AI Solution – This study suggests healthcare systems could use imaging AI-based biomarkers and EHR data to flag patients that might have undiagnosed conditions, allowing them to get these patients into care and identify/address larger systemic barriers. 

The Value-Based Care Justification – In addition to healthcare ethics reasons, the authors noted that imaging/EHR discrepancy detection could become increasingly financially important as we transition to more value-based models. AI/EHR analytics like this could be used to ensure at-risk patients are cared for as early as possible, healthcare disparities are addressed, and value-based metrics are achieved.

The Takeaway – Over the last year we’ve seen population health incidental detection emerge as one of the most exciting imaging AI use cases, while racial/demographic bias emerged as one of imaging AI’s most troubling challenges. This study managed to combine these two topics to potentially create a new way to address barriers to care, while giving health systems another tool to ensure they’re delivering value-based care.

MD Anderson’s Lung Cancer Blood Test

MD Anderson researchers developed a blood and risk-based test that could improve how we identify lung cancer screening candidates, potentially bringing more high-risk patients into screening while keeping more low-risk patients out.

The Blood + Risk Test – The test combines MD Anderson’s blood-based protein biomarker test with a lung cancer risk model that analyzes patient smoking history (the PLCOm2012 model). This combined test would be used to identify patients who should enroll in LD-CT screening programs.

The Study – MD Anderson researchers used the test to analyze 10k blood samples from 2,745 people with a +10 pack-year smoking history (including 1,299 samples from 552 people who developed cancer), finding that the blood + risk test:

  • Identified 105 of the 119 people diagnosed with cancer within one year
  • Beat the USPSTF 2021 criteria’s sensitivity (88.4% vs. 78.5%) and specificity (56.2% vs. 49.3%)
  • Identified 9.2% more lung cancer cases than the USPSTF criteria
  • Referred 13.7% fewer unnecessary screening patients than the USPSTF criteria

Blood-Based Momentum – Blood-based tests appear to be gaining momentum as a first-line cancer screening method, as the last 6 months brought a promising new MGH lung cancer test and a key validation milestone for the multi-cancer early detection blood test (MCED; detects 50 types of cancer).

The Takeaway – Although there’s still more research to be done, blood-based tests could bring more high-risk patients into LD-CT lung cancer screening programs, while reducing screening participation among patients who don’t actually need it. In other words, blood tests like these could address lung cancer screening’s two biggest challenges.

The AMA’s Quantitative Codes

The American Medical Association issued new CPT III codes for CT and MRCP quantification (see page 4), representing key milestones for quantitative and incidental/population health imaging.

CT Quantification Codes – The AMA’s 2022 CPT III update includes two new codes for quantitative CT tissue characterization, interpretation, and reporting. These codes could be a big deal for AI firms and radiology departments hoping to launch CT-based population health solutions (and eventually bill for them), such as Nanox AI’s HealthCCSng CAC scoring product and UCSF’s automated CAC scoring system. They also come just six months after the AMA added a similar CPT III code for using AI to automatically detect vertebral fractures in existing CT scans (covering Nanox AI’s VCF solution).

MRCP Quantification Codes – The AMA also added CPT III codes for quantitative magnetic resonance cholangiopancreatography (QMRCP) interpretation and reporting. These codes are a solid step for MRCP quantification products like Perspectum’s MRCP+ and could help drive adoption for this far less-subjective MRCP interpretation method. In the process, it might even change ERCP’s role to a purely therapeutic procedure.

About CPT III Codes – Since there’s often confusion about CPT III codes, it’s worth noting that they are intended to help collect clinical data for emerging technologies / procedures to support future coverage and regulatory decisions. CPT III codes don’t have assigned RVUs, so actual reimbursements would be up to payors.

The Takeaway

It’s pretty clear that the AMA is starting to see value in image quantification, AI, and incidental detection. In the last six months the AMA has issued four quantitative imaging CPT III codes, all of which directly support key imaging AI use cases (CT tissue characterization, CT vertebral fracture detection, ultrasound tissue characterization, MRI post-processing) and two that support key population health and incidental detection applications (CT tissue characterization, CT vertebral fracture detection). 

MaxQ AI Shuts Down

The Imaging Wire recently learned that MaxQ AI has stopped commercial operations, representing arguably the biggest consolidation event in imaging AI’s young history.

About MaxQ AI – The early AI trailblazer (founded in 2013) is best known for its Accipio ICH & Stroke triage platform and its solid list of channel partners (Philips, Fujifilm, IBM, Blackford, Nuance, and a particularly strong alliance w/ GE). 

About the Shutdown – MaxQ has officially stopped commercial operations and let go of its sales and marketing workforce. However, it’s unclear whether MaxQ AI is shutting down completely, or if this is part of a strategic pivot or asset sale.

Shutdown Impact – MaxQ’s commercial shutdown leaves its Accipio channel partners and healthcare customers without an ICH AI product (or at least one fewer ICH product), while creating opportunities for its competitors to step in (e.g., Qure.ai, Aidoc, Avicenna.ai). 

A Consolidation Milestone – MaxQ AI’s commercial exit represents the first of what could prove to be many AI vendor consolidations, as larger AI players grow more dominant and funding runways become shorter. In fact, MaxQ AI might fit the profile of the type of AI startups facing the greatest consolidation threat, given that it operated within a single highly-competitive niche (at least six ICH AI vendors) that’s been challenged to improve detection without slowing radiologist workflows. 

The Takeaway – It’s never fun covering news like this, but MaxQ AI’s commercial shutdown is definitely worth the industry’s attention. The fact is, consolidation happens in every industry and it could soon play a larger role in imaging AI.

Note: MaxQ AI’s shutdown unfortunately leaves some nice, talented, and experienced imaging professionals out of a job. Imaging Wire readers who are building their AI teams, should consider reaching out to these folks.

Home Ultrasound Goes Mainstream

Patients performing their own at-home ultrasound exams sounds like a pretty futuristic idea, but it’s becoming increasingly common in Israel due to a growing partnership between Clalit Health Services (Israel’s largest HMO) and DIY ultrasound startup Pulsenmore.

DIY Fertility Ultrasound – Clalit and Pulsenmore just signed an $11M agreement that will equip Clalit’s fertility treatment patients with thousands of Pulsenmore FC ultrasound systems over the next four years. The patients will use the Pulsenmore FC to perform self-exams during the IVF (in vitro fertilization) and fertility preservation processes and then transmit their scans to Clalit’s fertility clinicians. 

Pulsenmore Momentum – Pulsenmore previously provided Clalit with thousands of Pulsenmore ES fetal ultrasound systems, allowing expecting mothers to perform and transmit nearly 15k fetal ultrasounds since mid-2020. Pulsenmore also landed an interesting deal with Tel Aviv’s Sheba Medical Center in early 2021 that allowed pregnant women in Sheba’s COVID ward to perform their own fetal ultrasounds and transmit the scans to the hospital’s maternity ward.

Pulsenmore Potential – Pulsenmore’s early momentum is certainly helped by Israel’s unique healthcare system, but the company also has a European CE Mark (for the ES system), $40M in IPO funding, and ambitions to expand globally.

The Takeaway

The fact that thousands of ultrasounds are being used in Israeli homes shows that the home ultrasound concept has mainstream potential, and there’s a growing list of factors that could make it a reality. We’ve already seen a similar home system from Butterfly Network and a major industry trend towards smaller and easier to use ultrasounds (or even wearable), while the COVID pandemic has increasingly normalized at-home diagnostics and teleconsultations.

It will take some big changes for handheld ultrasounds to become MORE common than the stethoscope, but that idea doesn’t seem as ridiculous as it did a few years ago.

Imaging in 2022

Happy New Year, and welcome to the first Imaging Wire of 2022. For those of you working on your annual gameplans, here are some major imaging themes to keep in mind.

COVID Wave Watch – Nothing will have more influence on imaging in 2022 than how / when the COVID pandemic subsides, and how many more waves and variants emerge until we get there.

Efficiency Focus – It’s abundantly clear that imaging must become more efficient, making workflow improvements arguably the top priority for radiology teams and the folks who sell to them.

AI Matures – Imaging AI should mature at an even faster pace this year, bringing greater clinical adoption (and expectations), better workflow integration, improved use cases and business models, and the emergence of clear AI leaders. We’ll also likely see an initial wave of consolidation due to acquisitions and/or VC-prompted shutdowns.

More M&A – Imaging’s extremely active M&A climate should continue into 2022. Based on recent trends, this year’s M&A hotspots are likely to include PE-backed rad practice and imaging center acquisitions, enterprise imaging vendors adding to their tech and “ology” stacks, and more modality and solution expansions from the major OEMs.

Advanced Imaging Advancements – 2022 is shaping up to be a milestone year for MR and CT technology. On the MRI side, recent breakthroughs in magnet strength, helium requirements, portability, and image enhancement (among others) should lead to big changes in how / where MRI can be used. On the CT side, we’ll see OEMs increase their focus on achieving photon-counting CT leadership, even if most of that focus will be from their R&D and future product marketing teams in 2022.

The Patient Engagement Push – Digital patient engagement continues to gain momentum across healthcare, placing pressure on radiology teams to keep up. In 2022, that might mean getting better at radiology’s current patient engagement methods (e.g. image sharing, patient-friendly reporting, follow-up management), although patients’ expectations will likely evolve at an even faster pace.

Imaging Leaves the Hospital – A lot more imaging exams could be performed outside hospital walls in 2022, as payors continue to incentivize outpatient imaging (and image-related procedures) and at-home care continues its massive growth. 

While it’s hard to say which, if any, of these trends will be the top story of the next 12 months, it seems likely that we’re heading into another year with more big news than can fit into a seven-bullet roundup. Wishing you the best in 2022, Imaging Wire readers!

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-- The Imaging Wire team